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Authors
Advisor(s)
Abstract(s)
Energy is one of the most important production factors in farms, considering its impact on the profitability of the agricultural sector, its relationship with sustainability and the need for a green transition in agriculture to deal with the challenges created by climate change and the consequent global warming. In the green transition, it is important to replace fossil fuel sources with renewable energies and, in these contexts, the agricultural sector may make a double contribution, producing renewable energy and using more sustainable sources for the different processes and activities in the farms. Taking into account these motivations, this chapter proposes to select the models with better accuracy and the most relevant variables to predict the energy costs in the European Union farming sector. For that, machine learning models were considered, as well as statistical information from European Union databases. This chapter presents useful contributions to better understand the contexts associated with energy cost prediction in European farms.
Description
Keywords
Digital Era approaches Predictors European Union
Citation
Martinho, V.J.P.D. (2024). The Most Adjusted Predictive Models for Energy Costs. In: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-54608-2_7
Publisher
Springer, Cham